class_docstr: A kernel that takes nested structures as inputs.
  
  The inputs are flattened and concatenated, and then passed to the base kernel.
code: def __init__(self, kernel, feature_ndims=None):
      parameters = dict(locals())
      self._kernel = kernel
      if feature_ndims is None:
          feature_ndims = {
              "foo": kernel.feature_ndims,
              "bar": kernel.feature_ndims,
          }
      super(MultipartTestKernel, self).__init__(
          feature_ndims=feature_ndims,
          dtype={"foo": tf.float32, "bar": tf.float32},
          parameters=parameters,
          validate_args=kernel.validate_args,
      )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/internal/test_util.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
label_desc: Use image node_id 1 to create a new MultipartTestKernel object from inherited base classes: positive_semidefinite_kernel with example: obj = MultipartTestKernel(kernel, feature_ndims)